Memory-efficient DNN 
Recomputation [1]}, [2]}, [3]} and offloading [4]}, [5]}, [3]} is the favored method to reduce the footprint in training, but with significant overhead, e.g., memory swapping and extra computation. CDMA [7]}, Gist [8]}, JPAC-ACT [9]} and buddy compression [10]} compress the data by leveraging the sparsity or character of the feature map in particular neural network architecture to reduce the memory burden in the training stage. For the inference stage, many quantization [11]}, [12]}, [13]} and sparsity [14]}, [15]}, [16]}, [17]}, [18]} works have been proposed to reduce the parameters and computation of DNN.
